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Record W3129412479 · doi:10.1080/02678373.2021.1888561

Strategies addressing the limitations of cross-sectional designs in occupational health psychology: What they are good for (and what not)

2021· article· en· W3129412479 on OpenAlexaff
Toon W. Taris, Stacey R. Kessler, E. Kevin Kelloway

Bibliographic record

VenueWork & Stress · 2021
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsPsychologyCross-sectional studyApplied psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Like many researchers in other areas of the social and behavioural sciences, scholars in occupational health psychology (OHP) often rely on cross-sectional survey designs when addressing the issues...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.365
metaresearch head score (Gemma)0.480
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.635
Threshold uncertainty score0.783

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3650.480
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0060.007
Science and technology studies0.0040.005
Scholarly communication0.0070.011
Open science0.0060.007
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0090.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.318
GPT teacher head0.504
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations131
Published2021
Admission routes1
Has abstractyes

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